1.9.2 Future challenges in location discovery approaches:Research on localization in wireless sensor networks can be classified into twobroad categories centralized and distributed. These algorithms are given byresearchers but there are some aspects which we will consider as a challenge infuture.1.9.2.1 Distributed Localization: If each node collects partial data andexecutes the algorithm then localization algorithm is distributed.1.9.2.1.1 Beacon-based distributed algorithms: Categorized into three parts:• Diffusion: In diffusion the most likely position of the node is at thecentroid [22] of its neighboring known nodes. APIT requires a high ratio ofbeacons to nodes and longer range beacons to get a good position estimate. Forlow beacon density this scheme will not give accurate results.• Bounding box: Bounding box forms a bounding region for each nodeand then tries to refine their positions. The collaborative multilateration enablessensor nodes to accurately estimate their locations by using known beaconlocations that are several hops away and distance measurements to neighboringnodes. At the same time it increases the computational cost also.• Gradient: Error in hop count distance matrices in the presence of anobstacle.1.9.2.1.2- Relaxation-based distributed algorithms:The limitation of this approach is that the algorithm is susceptible to localminima [5].1.9.2.1.3- Coordinate system stitching based distributed algorithms:

21

Page 22

The advantage of this approach is that no global resources or communicationsare needed. The disadvantage is that convergence may take some time and thatnodes with high mobility may be hard to cover.

1.9.2.1.4- Hybrid localization algorithms:The limitation of this scheme is that it does not perform well when there areonly few anchors. SHARP gives poor performance for anisotropic network.1.9.2.1.5- Interferometric ranging based localization:Localization using this scheme requires considerably larger set of measurementwhich limits their solution to smaller network.1.9.2.2 Centralized Localization:If an algorithm collects localization related data from one station and execute itfrom the same station then it is called centralized. In centralized model theproblem is that if computing server fails due to some problem then entireprocessing goes down. Scalability is another problem when we consider thecentralized model for computation of our data. For security reasons thisapproach is also not best. The techniques which are based on centralized modelare explained below.• MDS-MAP:The advantage of this scheme is that it does not need anchor or beacon nodes tostart with. It builds a relative map of the nodes even without anchor nodes andnext with three or more anchor nodes, the relative map is transformed intoabsolute coordinates. This method works well in situations with low ratios ofanchor nodes. A drawback of MDS-MAP [10] is that it requires globalinformation of the network and centralized computation.22

Page 23

• Localize node based on Simulated Annealing:This algorithm does not propagate error in localization. The proposed flipambiguity mitigation method is based on neighborhood information of nodesand it works well in a sensor network with medium to high node density.However when the node density is low, it is possible that a node is flipped andstill maintains the correct neighborhood. In this situation, the proposedalgorithm fails to identify the flipped node

• A RSSI-based centralized localization technique:The advantage of this scheme is that it is a practical, self-organizing scheme thatallows addressing any outdoor environments [8]. The limitation of this schemeis that the scheme is power consuming because it requires extensive generationand need to forward much information to the central unit.1.9.2.3 Locally centralized approach:This approach is combination of both centralized and distributed approach. Ithas the advantage of both the approaches. It follows the cluster approach.Problem is that the system become more complex and need more computationfor handling the network.1.10 PROBLEM DEFINITIONThere are many localization schemes for the localization of wireless sensornetwork. Since each algorithm was developed to fulfill a different goal, theyvary widely in many parameters including accuracy, cost, size, configurability,security, and reliability [9, 10]. The node localization problem for wirelesssensor networks has received considerable attention, driven by the need toobtain higher location accuracy without incurring a large per-node cost(monetary cost, power consumption and form factor). Despite the exports made,no system has emerged as a robust, practical, solution for the node localizationproblem in realistic, complex, outdoor environments [11]. One such challenge ishow to accurately find the location of each sensor node, at a low cost. The nodelocalization problem has received tremendous attention from the research23

Page 24

community, thus emphasizing that it is an important problem and it’s hard toresolve this problem. Despite the attention the localization problem for wirelesssensor networks has received no universally acceptable solution for realistic,outdoor, environments. There are many problems regarding the localization butaccuracy and cost are more concerned.1.10.1 Accuracy: Accuracy is very important in the localization of wirelesssensor network. Higher accuracy is typically required in military installations,such as sensor network deployed for intrusion detection. However, forcommercial networks which may use localization to send advertisements fromneighboring shops, the required accuracy may not be lower.1.10.2 Cost: Cost is a very challenging issue in the localization of wirelesssensor network. There are very few algorithms which give low cost but thosealgorithms don’t give the high rate of accuracy.1.10.3 Power: Power is necessary for computation purpose. Power play a majorrole in wireless sensor network as each sensor device has limited power. Powersupplied by battery.1.10.4 Static Nodes: All static sensor nodes are homogeneous in nature. Thismeans that, all the nodes have identical sensing ability, computational ability,and the ability to communicate. We also assume that, the initial battery powersof the nodes are identical at deployment.1.10.5 Mobile Nodes: It is assumed that a few number of GPS enabled mobilenodes are part of the sensor network. These nodes are homogeneous in nature.But, are assumed to have more battery power as compared to the static nodesand do not drain out completely during the localization process. Thecommunication range of mobile sensor nodes are assumed not to changedrastically during the entire localization algorithm runtime and also not tochange significantly with in the reception of four beacon messages by aparticular static node.The following issues will be addressed in this thesis.24

Page 25

• How to develop a localization algorithm fulfilling the tight bounds ofresources.• How to get high accuracy and shortest path to solve localizationproblem in wireless sensor network.• How to approach the universal use of a single algorithm.The goal of this thesis is to provide a good solution for localization problems inthe wireless sensor networks with high accuracy and low cost which comeswhen the mobile beacon moves the shortest path in deployment area. The focuswill mainly be on accuracy in the localization of wireless sensor network as wellas low cost. Some existing algorithms for localization will be analyzed andpropose a new approach that can be used in more situations. Many of theexisting algorithms will be evaluated to understand the problem. A survey ofexisting algorithms for localization in wireless sensor networks will be made,and be used as a basis to design an algorithm that can be used in manysituations.In next chapter 2 we present related work in wireless sensor network. A lot ofresearch has been done in this area and much more is still needed because ofcost, power and accuracy are the constraints, where we need to improve. Wepropose an approach which works in both, range free and range based techniqueto improve all the constraints. In chapter 3 we present our proposed work whichis enhancement of previous composite approach. We propose an algorithm withmobile beacon shortest path to solve localization problem in wireless sensornetwork, we combined the proposed work with DV-hop method, calledEnhanced composite approach. This is unique approach which follow bothrange based and range free techniques in wireless sensor network. In chapter 4we have presented and compared the simulation results of enhanced compositeapproach with mobile beacon method, DV-hop method and previous compositeapproach. In chapter 5 we present conclusion and future scope of this enhancedcomposite approach.

25

Page 26

Chapter 2ALGORITHMS FOR LOCALIZATIONThe existing algorithm for localization can be broadly classified into two basiccategories:1. Range based techniques and Range Free Techniques.In range based mechanisms, the location of a sensor node can be determinedwith the help of the distance or angle metrics. These metrics are Time of Arrival(ToA), Time Difference of Arrival (TDoA), Angle of Arrival (AoA), ReceivedSignal Strength Indicator (RSSI). Range based techniques are highly accuratebut, they are equipped with highly expensive hardware and requires a lot ofcomputation. It increases the cost of the network and is inefficient in terms ofcomputations. The various range based techniques are Radio InterferometricMeasurement (RIM) [3], Multidimensional Scaling (MDS) [11], 3D -Landscape [14], DV-distance, DV-hop, Euclidean distance [15] etc. In rangefree techniques, the position of sensor node is identified on the basisinformation transmitted by nearby anchor nodes or neighboring nodes, based onhop or on triangulation basis. The various range free techniques are APIT [22],chord selection approach [2], three dimensional multilateration approach [5],SerLOC [6], centroid scheme [7] etc. Many more techniques are discussed in [4,8, 13, 15, 17, 18] . The range free techniques have an error in accuracy up to10% of the communication range of individual node [2]. But, these techniquesare much cheaper than the range based techniques.In [2], Ou and Ssu have proposed a range free localization approach for threedimensional wireless sensor networks. In this approach a GPS equipped flyinganchor is moved around a region under surveillance and it continuously26

Page 27

broadcasts its position information. These messages help other sensor nodes tocompute their location. This scheme was proved to be better than any existingrange free localization scheme for three dimensional wireless sensor networks.The basic assumption in this work is that the nodes are static. Thus, for everyrun of the algorithm the flying anchor will be required to fly in the network. As,the flying anchor node is not a participating node in the WSN, it is impracticalto be used in case of applications where sensors are more prone todisplacements. In such applications, the network needs to have the ability toself-localize, whenever required. For this purpose, we need have few GPSenabled sensor nodes within the region to be monitored. These nodes will helpother nodes to determine their location based on the positional informationabout themselves. Further, in case of any discrepancy, the sensor nodes maysend an error message to base station regarding its dislocation. The base stationwill generate a query message to other stations.The Global positioning system (GPS) enabled sensor nodes will broadcast theirlocations. With the help of this location information, the displaced node cancompute its new location. The above discussed strategy can be achieved by twoschemes:

1. Enable a few static sensor nodes in the network with GPS equippeddevices. These nodes will help in locating their neighbors depending on theplacement strategy. The rest of sensor nodes will collectively get localized withthe help of their respective neighboring nodes.

2. Take a few GPS enabled mobile sensor nodes to move within the networkand help in locating the other sensor nodes.The main drawback in using static sensor nodes is that, these nodes get theirlocation computed with the help of locations of their neighboring nodes asproposed in [5]. If there is an error while computing the nodes location, thiserror gets rippled in computations related to next tiers of neighbors and so on.Hence, the anchor nodes which are the most vital part of the localizing schememust be a part of the network and preferable mobile in nature.

27

Page 28

Here we will discuss some existing algorithms and techniques which are used tosolve localization problem in wireless sensor network. Each algorithm has somemerits and demerits, play important role as per the application requirement. Wediscuss here only two type of algorithm DV-hop and mobile beacon.

Localization with high accuracy and low cost in wireless sensor network is stillunder exploration. Only a few researchers have tried to address the problemusing both range based and range free techniques. S.Rao [20] addressed ascheme “A Composite approach to deal with localization problem in wirelesssensor network”. In this technique combined two approaches (A) DV- Hop and(B) Mobile beacon.

A. DV-HOP ALGORITHM FOR LOCALIZATION

DV- hop algorithm composed of 3 steps.1. Information of position at each node.2. Estimation of distance for one hop.3. Estimation of position.

Figure 2.1: DV-HOP analysis diagram.The first step of the algorithm is the flooding of the position of sources through28

Page 29

the network. Each node and sources record their hop-distance with other sourcesin the network. Sources try to find out the average length of one node and thisprocess is called hop correction [10]. The other nodes wait for the firstcorrection.It broadcast this information to the other nodes after accepting this. Now, thenodes do not need to wait any more and it directly goes to next step. In the laststep, the nodes try to find out their position according to number of hopsrecorded for each source and to the hop correction.Landmarks K1, K2, K3 are shown below in figures 2.1. Actual distancebetween landmarks is also mentioned. The landmarks calculate the averagedistance of each hop.• K1: (12+18)/ (3+4) =4.23.• K2: (12+10)/ (3+2) = 4.40.• K3: (10+18) / (2+4) = 4.67.The average distance can be used to correct the position. The node A is gettingits direction from K2. The distance can be obtained as:• A-K1: 2*4.4=8.8.• A-K2: 1*4.4=4.4.• A-K3: 2*4.4=8.8.Accuracy of this algorithm varies with the accuracy of distance between thehops. The node A gets the direction from the given landmarks as in figure 2.1but problem is that there are three different landmarks. So node A will get threecoordinates which is more ambiguous because A will get three distances fromits actual position in the mean time. The concept of beacon is used to solve thisproblem. More than three beacon nodes are needed to overcome this problem.Accuracy of this algorithm is very low because the average distance per-hop is arough estimation so it is difficult to find the accurate distance between the hops.This algorithm works properly when shapes are same or shapes are regular but itdoes not work for irregular shapes. This is the biggest drawback of thisalgorithm. There are many problems which need to be resolved. The solution of29

Page 30

these problems is available in the improved DV-Hop algorithm. DV-hopalgorithm is invalid for irregular shapes. The accuracy level is very low, and itis difficult to find out the actual distance between the hops.

B. MOBILE BEACON ALGORITHM FOR LOCALIZATION

This method is an alternative for centralized approach. A node which locatesitself is called Beacon node. A mobile beacon could be a human, machine, planeor any vehicle which is movable in deployment area. Trajectory is a pathfollowed by moving beacon in the deployment area. This algorithm is expensivebecause of GPS system. Both DV-hop and mobile beacon approaches have itsown merits and demerits. S.Rao [20] combined both approaches and designs acomposite approach which is a bit better than other two methods.

Mobile beacon trajectory

Mobile beaconUnknown nodes

Deployment area

Figure 2.2: mobile beacon trajectories

In mobile beacon approach a mobile beacon traverse the area randomly wheresensors are deployed. Using trilateration method some blind node estimate its